Gearbox Fault Diagnosis Method Research Articles

A meta transfer learning method for gearbox fault diagnosis with limited data

Intelligent diagnosis of mechanical faults is an important means to guarantee the safe maintenance of equipment. Cross domain diagnosis may lack sufficient measurement data as support, and this bottleneck is particularly prominent in high-end manufacturing. This paper presents a few-shot fault diagnosis methodology based on meta transfer learning for gearbox. To be specific, firstly, the subtasks for transfer diagnosis are constructed, and then joint distribution adaptation is conducted to align the two domain distributions; secondly, through adaptive manifold regularization, the data of target working condition is further utilized to explore the potential geometric structure of the data distribution. Meta stochastic gradient descent is explored to dynamically adjust the model’s parameter based on the obtained task information to obtain better generalization performance, ultimately to achieve transfer diagnosis of gearbox faults with few samples. The effectiveness of the approach is supported by the experimental datasets of the gearbox.

A Hierarchical Fault Diagnosis Model for Planetary Gearbox with Shift-invariant Dictionary and OMPAN

Abstract Planetary gearbox has been widely applied in the mechanical transmission system, and the failure types of planetary gearbox are more and more diversified. The conventional fault diagnosis methods focus on identifying the faults in the fault library, but ignored the faults outside the fault library. However, it is impossible to build a fault library for all failure types. Targeting the problem of identifying the faults outside the fault library, a hierarchical fault diagnosis method for planetary gearbox with shift-invariant dictionary and orthogonal matching pursuit with adaptive noise (OMPAN) is proposed in this paper. By k-means singular value decomposition (K-SVD) dictionary learning method and shift-invariant strategy, a shift-invariant dictionary is constructed so that the normal modulation components of signals can be completed decomposed. OMPAN algorithm is proposed, which uses the white Gaussian noise to improve the solution method of the orthogonal matching pursuit (OMP) algorithm so that it can separate the modulation components in the signal more accurately. The fault feature extraction is developed via shift-invariant dictionary and OMPAN. A hierarchical classifier is proposed with 3 sub-classifiers so that both the faults in the fault library and the faults outside the fault library are identified. The effectiveness of the proposed hierarchical fault diagnosis method is validated by a planetary gearbox experiment. Result show that the proposed shift-invariant dictionary and OMPAN method has achieved a superior performance in highlighting fault features compared with other two sparse decomposition methods. The proposed hierarchical fault diagnosis approach has achieved a good performance both in classification of the faults in the fault library and identification of the faults outside the fault library.

Fault diagnosis of gearboxin wind turbine based on EMD-DCGAN

INTRODUCTION: Wind turbine gearbox fault diagnosis is of great significance for the safe and stable operation of wind turbines. The accuracy of wind turbine gearbox fault diagnosis can be effectively improved by using complete wind turbine gearbox fault data and efficient fault diagnosis algorithms.A wind turbine gearbox fault diagnosis method based on EMD-DCGAN method is proposed in this paper.
 OBJECTIVES: It can solve the problem when the sensor fails or the data transmission fails, it will lead to errors in the wind turbine gearbox fault data, which in turn will lead to a decrease in the wind turbine gearbox fault diagnosis accuracy.
 METHODS: Firstly, the outliers in the sample data need to be detected and removed. In this paper, the EMD method is used to eliminate outliers in the wind turbine gearbox fault data samples with the aim of enhancing the true continuity of the samples; secondly, in order to make up for the lack of missing samples, a data enhancement algorithm based on a GAN network is proposed in the paper, which is able to effectively perfect the missing items of the sample data; lastly, in order to improve the accuracy of wind turbine gearbox faults, a DCGAN neural network-based fault diagnosis method is proposed, which effectively combines the data dimensionality reduction feature of deep learning method and the data enhancement feature of generative adversarial network, and can improve the accuracy and speed of fault diagnosis.
 RESULTS and CONCLUSIONS: The experimental results show that the proposed method can effectively identify wind turbine gearbox fault conditions, and verify the effectiveness of the algorithm under different sample data conditions.

A fault diagnosis method for variable speed planetary gearbox based on ADGADF and Swin Transformer

The vibration signal of planetary gearboxes under variable speed conditions shows non-stationary characteristics, indicating that fault diagnosis has become more complex and challenging. In order to more accurately diagnose faults in planetary gearboxes under variable speed conditions, a new method is proposed based on the angular domain Gramian angular difference field (ADGADF) and Swin Transformer. This method initially employs the chirplet path pursuit (CPP) algorithm to fit the speed curve of the original time-domain signal and then combines the speed curve with computed order tracking (COT) to achieve equal angle resampling of the time-domain signal, obtaining a stationary signal in the angular domain. On the basis of the above, the angular domain signal is creatively encoded into the two-dimensional images using the Gramian angular field (GAF), which accurately represents the fault characteristics of the original signal. Finally, the Swin Transformer network, with efficient global feature extraction capability, is used to learn advanced features from the images, achieving accurate fault recognition and classification. The proposed method is verified by experiment on the planetary gearbox and its performance is compared with several common coding methods and intelligent diagnosis algorithms. The experimental results show that the proposed method reaches an accuracy of up to 99.8%. In addition, its performance in accuracy, precision, recall, F1-score and the confusion matrix is superior to traditional diagnostic methods. It also offers the advantage of strong robustness.

Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model

Because the gearbox in transmission systems is prone to failure and the fault signal is not obvious, the fault end cannot be located. In this paper, a gearbox fault diagnosis method grounded on improved complete ensemble empirical mode decomposition with adaptive noise, a multiscale permutation entropy and adaptive wavelet thresholding (ICEEMDAN-MPE-AWT) denoising method and an SE-ResNeXt50 transfer learning model are proposed. Initially, the vibration signal is denoised by ICEEMDAN-MPE-AWT, the denoised vibration signal is then converted into a Gram angle field (GAF) diagram, and then the parameters are transferred by the fine-tuning transfer learning strategy. Finally, a GAF diagram is input into the model for training to achieve fault extraction and classification. In this paper, the open gear dataset of Southeast University is used for experimental research. The experimental results show that when using the ICEEMDAN-MPE-AWT and when the signal-to-noise ratio (SNR) of the experimental data is −4 dB, the average accuracy of the GASF+TSE-ResNeXt50 and the GASF+TSE-ResNeXt18 can reach 98.8% and 97.5%, respectively. When the SNR is 6 dB, the accuracy of the above two models reaches 100% and 99.3%, respectively. Moreover, when compared to alternative approaches, the noise reduction method in this paper can better remove noise interference so that the model can better extract fault features. Therefore, the method proposed in this article shows significant improvement in noise reduction and fault classification accuracy compared to other methods.

A TLOT train gearbox fault diagnosis method based on ridge extraction under variable speed conditions

Owing to the rapidly varying working conditions of urban rail trains, the rotational speed conditions constantly shift in a short time span. As a key component of the running gear, the gearbox generates non-stationary vibration signals, making it challenging to monitor its health status. To address this challenge, a tacholess order tracking method (TLOT) based on ridge extraction method is proposed in this paper. This method determines the optimal search starting point using the time-frequency Gini coefficient and extracts the time-varying gearbox meshing frequency components from the time-frequency representation results. Furthermore, the TLOT method is utilized to process the strongly time-varying gearbox signal. In the order domain, multiple frequency components are extracted to reconstruct the signal. Simulation and experimental results demonstrate that the proposed method achieves a superior ridge extraction effect on gearbox experimental signals. It accurately converts unstable signals into angular domain stable signals, enhancing the energy aggregation of time-varying unstable signals in the order domain. This approach addresses the problem of weak harmonic component extraction from gearbox signals and effectively reduces interference in the resonance band, realizing fault diagnosis of the gearbox under unstable working conditions.

Gearbox fault diagnosis method based on improved semi-supervised MTDL and GAF

Aiming at the problem that it takes a long time and high cost to obtain complete labeled data under intelligent fault diagnosis and unlabeled data is not used. This paper proposes an improved semi-supervised mean teacher deep learning (MTDL) and Gramian angle field (GAF) fusion diagnostic method. This method fully utilizes a small number of labeled samples and a large number of unlabeled samples to deeply mine invisible fault features and potential physical correlations. At the same time, it solves the problem of losing the inter-data correlation structure when one-dimensional time series signals are used as inputs for neural networks. The GAF-MTDL method uses consistency regularization and modifies the network structure in the mean teacher algorithm into a semi-supervised deep learning model enhanced by WideResNet. The experimental results show that the proposed GAF-MTDL method saves a lot of manual labeling costs, improves the recognition accuracy and generalization ability, and can achieve excellent prediction accuracy with very little labeled data. In the end, the accuracy of planetary gear fault identification reached 98.22% under the labeling rate of 20%, and the accuracy of fault identification reached 99.98% through the verification of the bearing data set of Case Western Reserve University. The value of this research is to bring an efficient and low-cost technology to the field of industrial intelligent fault diagnosis, which can significantly improve the accuracy of fault identification.

Multivariate multi-scale cross-fuzzy entropy and SSA-SVM-based fault diagnosis method of gearbox

Abstract Fuzzy entropy (FuzzyEn) is widely recognized as a powerful tool for analyzing nonlinear dynamics and measuring the complexity of time series data. It has been utilized as an effective indicator to capture nonlinear fault features in gearbox vibration signals. However, FuzzyEn only measures complexity at a single scale, ignoring the valuable information contained in large-scale features of the time series. Furthermore, FuzzyEn does not account for coupling characteristics between related or synchronized time series. To address these limitations, a novel entropy-based approach called multivariate multi-scale cross-fuzzy entropy (MvMCFE) is proposed in this paper for measuring the complexity and mutual predictability of two multivariate time series. Relying on the advantages of MvMCFE in nonlinear feature extraction, a new fault diagnosis method for gearboxes is proposed based on MvMCFE and an optimized support vector machine (SVM) using the salp swarm algorithm (SSA-SVM). Ultimately, the proposed gearbox diagnostic method is employed to analyze the gearbox experimental data and a comparison with existing fault diagnosis approaches is conducted. The comparison results indicate that the proposed method can effectively extract nonlinear fault features of gearboxes and achieve the highest recognition rate compared to the other methods.

Fault feature extraction and diagnosis method for gearbox under variable operating conditions

In this paper, a fault feature extraction method based on the combination of computational order tracking (COT) and variational mode decomposition (VMD) is proposed to solve the problem of fault feature extraction in a gearbox under variable speed conditions. First, a speed estimation method based on a forward-backward greedy algorithm and Fourier fitting is proposed to solve the speed estimation problem under variable speed conditions. Then, a feature extraction method based on COT, VMD, and fast kurtometry is proposed. COT is used to calculate the order. After the signal is decomposed by VMD, a fast kurtogram is used to filter the modal components with obvious fault features so as to extract fault features. Finally, combined with the analysis of actual equipment examples, the experimental results show that steady-state filtering can effectively filter the background noise and improve the signal-to-noise ratio. The rotation speed estimated by the forward–backward greedy algorithm and Fourier fitting is very close to the actual speed, which verifies that the method proposed in this paper can effectively solve the problem of fault feature extraction of a gearbox under variable speed conditions.

Gearbox fault diagnosis method based on lightweight channel attention mechanism and transfer learning

In practical engineering, the working conditions of gearbox are complex and variable. In varying working conditions, the performance of intelligent fault diagnosis model is degraded because of limited valid samples and large data distribution differences of gearbox signals. Based on these issues, this research proposes a gearbox fault diagnosis method integrated with lightweight channel attention mechanism, and further realizes the cross-component transfer learning. First, time–frequency distribution of original signals is obtained by wavelet transform. It could intuitively reflect local characteristics of signals. Secondly, based on a local cross-channel interaction strategy, a lightweight efficient channel attention mechanism (LECA) is designed. The kernel size of 1D convolution is affected by channel number and coefficients. Multi-scale feature input is used to retain more detailed features of different dimensions. A lightweight convolutional neural network is constructed. Finally, a transfer learning method is applied to freeze lower structures of the network and fine-tune higher structures of the model using small samples. Through experimental verification, the proposed model could effectively utilize samples. The application of transfer learning could realize accurate and fast fault classification of small samples, and achieve good gearbox fault diagnosis effect under varying working conditions and cross-component conditions.

Fault diagnosis method for planetary gearbox based on intrinsic feature extraction and attention mechanism

In a high-noise environment and with a limited number of faulty samples, it becomes challenging to extract a sufficient amount of useful fault information, which makes gear fault diagnosis more difficult. To address these issues, this paper proposes a fault diagnosis method for planetary gearboxes based on intrinsic feature extraction and attention mechanism. The method utilizes the complementary ensemble empirical mode decomposition algorithm to perform modal decomposition on the fault vibration signal, obtaining a series of modal components. By comparing and selecting the modal components that contain a significant amount of fault features, they are then transformed into two-dimensional images with time–frequency properties using wavelet transform. Additionally, a neural network model based on attention mechanism and large-scale convolution is proposed. The preprocessed images are inputted into the network for feature extraction. During this process, the large-scale convolution with residual structure maximizes the retention of effective feature information, while the attention network further filters the features. Finally, the selected features are used for fault classification. The model is validated using the gear datasets from Southeast University and the University of Connecticut. A comparison is made with the Pro-MobileNetV3, channel attention and multiscale convolutional neural network, multiscale dynamic adaptive residual network, and CBAM-ResNeXt50 models. It is found that the accuracy reaches 100% before adding Gaussian noise and 99.68% after adding noise, which is significantly higher than that of other models.

Research on Gearbox Fault Diagnosis Method Based on VMD and Optimized LSTM

The reliability of gearboxes is extremely important for the normal operation of mechanical equipment. This paper proposes an optimized long short-term memory (LSTM) neural network fault diagnosis method. Additionally, a feature extraction method is employed, utilizing variational mode decomposition (VMD) and permutation entropy (PE). Firstly, the gear vibration signal is subjected to feature decomposition using VMD. Secondly, PE is calculated as a feature quantity output. Next, it is input into the improved LSTM fault diagnosis model, and the LSTM parameters are iteratively optimized using the chameleon search algorithm (CSA). Finally, the output of the fault diagnosis results is obtained. The experimental results show that the accuracy of the method exceeds 97.8%.

Research on a novel fault diagnosis method for gearbox based on matrix distance feature

Aiming at the problem of fault diagnosis and classification of rolling bearing and gear of gearboxes, a novel method based on matrix distance features of Gramian angular field (GAF) image is proposed based on sliding window compressible GAF transformation. The method converts the one-dimensional fault signal into a two-dimensional feature matrix and constructs the discrimination matrix of each fault category by establishing the mean value of the feature matrix of a priori samples. For the new sampled signal, after converting it into a two-dimensional feature matrix, the feature matrix is obtained. The fault classification is carried out by using the matrix distance between feature matrix and the discrimination matrix of each category. The method is validated by the test data of Case Western Reserve University and the acoustic emission data from a gearbox test bench. The classification accuracy is 99.17% and 95.71%, which presented the feasibility and effectiveness of the novel method proposed in this paper.

Multi-sensor gearbox fault diagnosis using generalized minimum entropy deconvolution and main frequency center extraction

Accurate diagnosis of gearbox faults is vital to maintain the stability and dependability of rotating machinery. In practical engineering scenarios, the installation of multiple sensors is often necessary to monitor numerous variables and gather extensive information. To effectively utilize the fault information from multiple sensors and improve fault diagnosis performance, this paper presents a gearbox fault diagnosis method using generalized minimum entropy deconvolution (GMED) and main frequency center extraction (MFCE). Firstly, the introduced GMED enhances the fault signals from multiple sensors through the maximization of standardized moments, offering advantages such as stability, wide applicability, and minimal parameter tuning requirements. Subsequently, the proposed MFCE is employed to extract features. MFCE selects main spectral lines and derives a novel feature metric named main frequency center. This not only further reduces the influence of interfering components, but also requires the extraction of only 2 n features (where n is the number of sensors) to achieve a high fault recognition rate, while remaining compatible with multiple classical classifiers. Finally, the effectiveness and robustness of the proposed method are demonstrated through experimental results on two gearbox fault datasets.

A structurally re-parameterized convolution neural network-based method for gearbox fault diagnosis in edge computing scenarios

Gearboxes operate in harsh environments. Cloud-based techniques have been previously adopted for fault diagnosis in Gearboxes. Cloud-based fault diagnosis methods are prone to time delays and loss of information. Therefore, edge computing-based fault diagnosis becomes an option. However, with limited hardware resources for edge devices, balancing the diagnostic capabilities of the model with operating performance becomes a challenge. This paper proposes a lightweight convolutional neural network for gearbox fault diagnosis in edge computing scenarios to achieve an accurate diagnosis and lightweight deployment of models. By constructing the Mel-Frequency Cepstral Coefficients (MFCC) feature matrix of input data, the methodology can suppress noise interference and improve diagnostic accuracy. Based on the structural re-parameterization, the model structure transforms from multiple branches at training time to a single branch at inference time. This improves the inference speed of the model and reduces the hardware cost when the model is deployed while ensuring that the diagnostic capability of the model remains unchanged. Validation experiments were conducted on a public dataset and a custom experimental device, using the NVIDIA Jetson Xavier NX kit as the edge computing platform. According to the experiment result, after extracting the MFCC feature matrix, the average diagnostic accuracy rate in the noisy environment of the presented methodology is improved by 12.22% and 9.44%, respectively. After structural re-parameterization, the Memory of the model decreases by 52.58%, and the inference speed is increased by 38.83%.

Fault diagnosis of gearbox based on Fourier Bessel EWT and manifold regularization ELM

The novel fault diagnosis method of gearbox based on Fourier Bessel series expansion-based empirical wavelet transform (FBEWT) and manifold regularization extreme learning machine (MRELM) is proposed to obtain excellent fault diagnosis results of gearbox in this paper. A new feature extraction strategy based on Fourier Bessel series expansion-based empirical wavelet transform is used to capture the key non-stationary features of the vibrational signal of gearbox, and significantly improve the diagnosis ability of gearbox. The ELM with manifold regularization is proposed for fault diagnosis of gearbox. In order to outstand the superiority and stability of the proposed FBEWT and manifold regularization ELM, the balanced dataset and unbalanced dataset, respectively, are used. The experimental results testify that FBEWT-MRELM are more superior and stable than FBEWT-ELM, EWT-MRELM, and EWT-ELM regardless of balanced dataset and unbalanced dataset.

Intelligent Fault Diagnosis of Gearbox Under Variable Working Conditions With Adaptive Intraclass and Interclass Convolutional Neural Network.

The industrial gearboxes usually work in harsh and variable conditions, which results in partial failure of gears or bearings. Accordingly, the continuous irregular fluctuations of gearbox under variable conditions maybe increase the intraclass difference and reduce the interclass difference for the monitored samples. To this end, a new intelligent fault diagnosis method of gearbox based on adaptive intraclass and interclass convolutional neural network (AIICNN) under variable working conditions is proposed. The core of the proposed algorithm is to apply the designed intraclass and interclass constraints to improve the distribution differences of samples. Meanwhile, the adaptive activation function is added into the 1-D convolutional neural network (1dCNN) to enlarge the heterogeneous distance and narrow the homogeneous distance of samples. Specifically, the training sample subset with intraclass and interclass spacing fluctuations under variable conditions is first converted into frequency domain through the fast Fourier transform (FFT), and the designed AIICNN algorithm is employed for model training. Afterward, the testing subset is provided to the trained AIICNN algorithm for fault diagnosis. The experimental data of the planetary gearbox test rig verify the feasibility of the proposed diagnosis method and algorithm. Compared with other methods, this method can eliminate the difference of sample distribution under variable conditions and improve its diagnostic generalization.

Fault Diagnosis Method of Planetary Gearbox Based on ISSA-VME and Multi-scale Fuzzy Entropy

To solve the problem of noise interference and difficulty in feature extraction of vibration signals of planetary gearbox, propose a fault diagnosis method of planetary gearbox based on improved sparrow search algorithm optimized variational mode extraction (ISSA-VME) and multi-scale fuzzy entropy (MFE). The vibration signal was decomposed by ISSA-VME, and the multi-scale fuzzy entropy of the original signal was calculated according to the IMF component. The fault feature set composed of MFE was applied to the SVM classifier optimized by the improved sparrow search algorithm for fault pattern recognition. The experimental results demonstrate that compared with ICEEMDAN and VMD methods, ISSA-VME has a better effect and higher efficiency on vibration signal decomposition. The identification rate of missing teeth, cracks, wear, and other faults of planetary gearbox sun gear by the proposed method reaches 97.50%.

Gearbox Fault Diagnosis Based on Optimized Stacked Denoising Auto Encoder and Kernel Extreme Learning Machine

The gearbox is one of the key components of many large mechanical transmission devices. Due to the complex working environment, the vibration signal stability of the gear box is poor, the fault feature extraction is difficult, and the fault diagnosis accuracy makes it difficult to meet the expected requirements. To solve this problem, this paper proposes a gearbox fault diagnosis method based on an optimized stacked denoising auto encoder (SDAE) and kernel extreme learning machine (KELM). Firstly, the particle swarm optimization algorithm in adaptive weight (SAPSO) was adopted to optimize the SDAE network structure, and the number of hidden layer nodes, learning rate, noise addition ratio and iteration times were adaptively obtained to make SDAE obtain the best network structure. Then, the best SDAE network structure was used to extract the deep feature information of weak faults in the original signal. Finally, the extracted fault features are fed into KELM for fault classification. Experimental results show that the classification accuracy of the proposed method can reach 97.2% under the condition of low signal-to-noise ratio, which shows the effectiveness and robustness of the proposed method compared with other diagnostic methods.

Gearbox Fault Diagnosis Based on Multi-Sensor Deep Spatiotemporal Feature Representation

The vibration signal acquired by a single sensor contains limited information and is easily interfered by noise signals, resulting in the inability to fully express the operating characteristics and state of a gearbox. To address this problem, our study proposes a gearbox fault diagnosis method based on multi-sensor deep spatiotemporal feature representation. This method utilizes two vibration sensors to obtain the vibration information of the gearbox. A fault diagnosis model (PCNN–GRU) combined with a parallel convolutional neural network (PCNN) and gated recurrent unit (GRU) was used to fuse the gearbox vibration information. The parallel convolutional neural network was used to extract the spatial information of the vibration signals collected by different position sensors, and the timing information was mined through the gated recurrent unit. The deep spatiotemporal features that fuse the multi-sensor spatial and temporal information were composed. The collected multi-sensor vibration signals were directly input into the PCNN–GRU model, and an end-to-end intelligent diagnosis of the gearbox faults was realized. Finally, through experimental verification, the accuracy rate of this model can reach up to 99.92%. Compared with other models, this model has a higher diagnostic accuracy and stability.